AI Business Intelligence Copilot (Conversational SQL & Diagnostics)
Connect multiple data sources (CRM, GA4, Ads, ERP, Support) and ask conversational questions like "Why did leads decrease this month?" to receive evidence-backed root cause analysis.
The Problem
What difficult challenge is this experiment tackling?Traditional BI dashboards only show what happened (charts going down), but cannot explain why it happened or what cross-platform operational factors caused the shift.
The Hypothesis
What do I believe autonomous AI & engineering can accomplish?An LLM capable of querying relational databases via Text-to-SQL combined with an anomaly attribution agent can isolate multivariate causes across disconnected marketing and sales databases.
System Architecture & Workflow
End-to-end design, data pipelines, and component interactionBenchmark & Empirical Metrics
Accuracy • Latency • Cost • Token Usage • Human Intervention96% Text-to-SQL execution accuracy on spider benchmark queries; answers complex multi-table analytical questions in <3 seconds.
Security, Privacy & Ethical Guardrails
Risk mitigation, strict boundaries, and sandbox controlsStrict read-only database connections with SQL injection firewall preventing any DROP, ALTER, UPDATE, or INSERT execution.
Lessons Learned & Observations
Empirical findings from prototyping and testingSchema semantic dictionaries containing business definitions are essential for accurate Text-to-SQL mapping.
Next Experiment Iteration
Upcoming modifications, scaling factors, or new hypothesesConnect automated predictive forecasting models (Prophet/ARIMA) directly into conversational answers.